Stories with Tag CPython

• The first year of free-threaded Python

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  Posted: 2025-05-16 09:42:31

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  One year after the "Free the GIL" project began, significant progress has been made towards enabling true parallelism in CPython. The project, focused on making the Global Interpreter Lock (GIL) optional, has seen successful integration of the "nogil" branch, demonstrating substantial performance improvements in multi-threaded workloads. While still experimental and requiring code adaptations for full compatibility, benchmarks reveal impressive speedups, particularly in numerical and scientific computing scenarios. The project's next steps involve refinement, continued performance optimization, and addressing compatibility issues to prepare for eventual inclusion in a future CPython release. This work paves the way for a significantly faster Python, particularly beneficial for CPU-bound applications.

  This blog post, titled "The first year of free-threaded Python," published by Quansight Labs, reflects on the one-year anniversary of the "Faster CPython" project's substantial progress in enabling true parallelism in Python. This initiative, generously funded by a grant from Meta, aims to significantly enhance the performance of CPython, the default and most widely used implementation of the Python programming language.

  The core of the project revolves around removing the Global Interpreter Lock (GIL), a long-standing mechanism in CPython that has historically limited true parallel execution of Python bytecode. While Python has offered multi-processing capabilities, the GIL prevented multiple native threads within a single process from executing Python bytecode concurrently. This limitation hampered performance, especially on multi-core processors, as only one thread could access the Python interpreter at any given time.

  The blog post highlights the significant milestones achieved during the first year of the project. This includes the successful implementation of a per-interpreter GIL, often referred to as the "nogil" build, which effectively eliminates the global constraint of the GIL. The post elaborates on the technical challenges involved in this undertaking, including ensuring compatibility with existing C extensions, managing memory allocation, and maintaining the stability and integrity of the Python runtime environment. Specific examples of the complex interactions between the GIL removal and garbage collection are discussed, illustrating the intricacies of the project.

  Furthermore, the post emphasizes the collaborative nature of the project, highlighting the contributions of numerous developers, both within and outside the core CPython development team. It also underscores the rigorous testing and benchmarking efforts undertaken to evaluate the performance gains and ensure the stability of the "nogil" build. The impressive benchmarks showcased demonstrate significant performance improvements in multi-threaded workloads, suggesting the potential for substantial speedups in various Python applications.

  Looking ahead, the post outlines the future roadmap for the project, including plans for further optimization, refinement, and ultimately, integration of the "nogil" build into the mainline CPython release. This transition, as indicated in the post, will likely be a gradual process, involving multiple stages and careful consideration of backward compatibility. The ultimate goal is to make the performance benefits of free-threaded Python accessible to the wider Python community, empowering developers to leverage the full potential of modern multi-core hardware. The post concludes with a call to action, encouraging community involvement in testing and providing feedback on the "nogil" build, thus contributing to the successful realization of a truly free-threaded Python.

  • Python
  • Threading
  • GIL
  • Global Interpreter Lock
  • performance
  • concurrency
  • Parallelism
  • CPython
  • nogil
  • Software Development
  • Programming Languages
  • Open Source
  • Quansight
  • Multithreading

  Summary of Comments ( 147 )
  https://news.ycombinator.com/item?id=44003445

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  Hacker News users generally expressed enthusiasm for the progress of free-threaded Python and the potential benefits of faster Python code execution. Some commenters questioned the practical impact for typical Python workloads, emphasizing that GIL removal mainly benefits CPU-bound multithreaded programs, which are less common than I/O-bound ones. Others discussed the challenges of ensuring backward compatibility and the complexity of the undertaking. Several mentioned the possibility of this development ultimately leading to a Python 4 release, breaking backward compatibility for substantial performance gains. There was also discussion of alternative approaches, like subinterpreters, and comparisons to other languages and their threading models.

  The Hacker News post "The first year of free-threaded Python" (linking to a Quansight Labs blog post recapping the first year of the "free-threaded Python" project) generated a moderate number of comments, mostly focusing on the complexities of achieving true parallelism in Python and the nuances of the project's approach.

  Several commenters discussed the historical challenges and current state of parallelism in CPython, with mentions of the Global Interpreter Lock (GIL) and its impact on multi-threaded performance. One commenter highlighted the distinction between "free-threaded" and "parallel," emphasizing that eliminating the GIL doesn't automatically guarantee parallel execution due to other potential bottlenecks. They elaborated that true parallelism requires careful consideration of memory management and data structures.

  Another commenter pointed out the trade-offs involved in removing the GIL, specifically the potential performance regressions for single-threaded code. They questioned whether the benefits of parallelism would outweigh the costs for the average Python user. This sparked a small thread discussing the target audience for this project, with the suggestion that it's primarily aimed at specific use cases with high parallelism demands, rather than general-purpose Python programming.

  One comment expressed skepticism about the practicality of achieving significant performance improvements in Python, referencing previous attempts and the inherent limitations of the language's design. However, another commenter countered this by highlighting the potential of this particular project, suggesting it offers a more promising approach compared to previous efforts.

  A few commenters inquired about the compatibility of this project with existing Python code and libraries, expressing concerns about potential breakage. There was also some discussion about alternative approaches to parallelism in Python, such as multiprocessing and asynchronous programming, and how they compare to the "free-threaded" approach.

  Finally, some comments simply expressed interest in the project and its potential implications for the future of Python, acknowledging the complexity of the undertaking but recognizing its potential value. Overall, the comments reflect a cautious optimism tempered by an understanding of the long-standing challenges associated with Python parallelism.

• Performance of the Python 3.14 tail-call interpreter

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  Posted: 2025-03-10 06:44:27

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  Python 3.14 introduces an experimental, limited form of tail-call optimization. While not true tail-call elimination as seen in functional languages, it optimizes specific tail calls within the same frame, significantly reducing stack frame allocation overhead and improving performance in certain scenarios like deeply recursive functions using accumulators. The optimization specifically targets calls where the last operation is a call to the same function and local variables aren't modified after the call. While promising for specific use cases, this optimization does not support mutual recursion or calls in nested functions, and it is currently hidden behind a flag. Performance benchmarks reveal substantial speed improvements, sometimes exceeding 2x, and memory usage benefits, particularly for tail-recursive functions previously prone to exceeding recursion depth limits.

  Nelson Elhage's blog post, "Performance of the Python 3.14 tail-call interpreter," dives deep into the performance implications of the newly introduced tail-call optimization in CPython 3.14. Elhage meticulously examines the performance characteristics of this optimization, focusing on the specific scenarios where it yields benefits and the situations where it falls short.

  The post begins by establishing the context of tail-call optimization, explaining that it targets function calls occurring at the tail position of a function – meaning the call is the very last operation performed before returning. In such cases, theoretically, the current stack frame can be reused for the called function, avoiding stack growth and enabling efficient recursion. However, CPython's implementation, due to the complexities of the interpreter and its bytecode, faces limitations.

  Elhage employs rigorous benchmarking to evaluate the performance impact. He leverages a factorial function implemented recursively, both with and without tail-call optimization, serving as a prime example of a tail-recursive algorithm. The benchmarks explore varying recursion depths and compare the performance against iterative implementations. Critically, Elhage doesn't stop at simple microbenchmarks; he also incorporates more realistic scenarios involving generators and asynchronous functions to provide a holistic view.

  The results reveal that tail-call optimization in CPython 3.14 does indeed offer performance gains in specific circumstances. For deep tail-recursive functions, the optimization successfully prevents stack overflows, allowing the execution to complete where it would otherwise fail. However, even with the optimization, tail recursion doesn't magically become faster than iteration. In fact, the optimized tail-recursive implementation remains notably slower than its iterative counterpart. Elhage attributes this performance gap to the inherent overhead associated with function calls in Python, an overhead that persists even with tail-call optimization.

  Furthermore, the benchmarks demonstrate that the optimization yields little to no benefit in scenarios involving generators and async functions. Elhage explains this by highlighting the fact that these constructs already employ mechanisms to manage their execution state efficiently, thereby mitigating the need for tail-call optimization to prevent stack growth.

  In conclusion, Elhage's analysis paints a nuanced picture of CPython 3.14's tail-call optimization. While it successfully prevents stack overflows in deep tail recursion, it doesn't make tail recursion inherently faster than iteration. The optimization's benefits are most prominent in pure tail-recursive scenarios, whereas its impact on generators and async functions is negligible. The post provides valuable insights into the practical implications of this new feature, empowering Python developers to understand its strengths and limitations.

  • Python
  • Python 3.14
  • CPython
  • interpreter
  • performance
  • Tail Call Optimization
  • Tail Call Elimination
  • Recursion
  • Stack Frames
  • optimization
  • Programming Languages
  • Software Development

  Summary of Comments ( 111 )
  https://news.ycombinator.com/item?id=43317592

  Verbose tl;dr

  HN commenters largely discuss the practical limitations of Python's new tail-call optimization. While acknowledging it's a positive step, many point out that the restriction to self-recursive calls severely limits its usefulness. Some suggest this limitation stems from Python's frame introspection features, while others question the overall performance impact given the existing bytecode overhead. A few commenters express hope for broader tail-call optimization in the future, but skepticism prevails about its wide adoption due to the language's design. The discussion also touches on alternative approaches like trampolining and the cultural preference for iterative code in Python. Some users highlight specific use cases where tail-call optimization could be beneficial, such as recursive descent parsing and certain algorithm implementations, though the consensus remains that the current implementation's impact is minimal.

  The Hacker News post discussing CPython 3.14's tail-call interpreter performance has a moderate number of comments, exploring various aspects of the change.

  Several commenters express skepticism about the practical benefits of tail-call optimization in Python, given the language's existing idioms and the potential disruption to debugging. One commenter points out that Python's reliance on stack traces for debugging makes proper tail-call elimination problematic, potentially hindering troubleshooting. Others echo this sentiment, suggesting that full tail-call optimization might not align well with Python's design philosophy. The cost of maintaining stack information for debugging is discussed, with a suggestion that perhaps a hybrid approach, selectively applying optimization, might be more suitable.

  Another thread of discussion revolves around the limitations and potential downsides of the proposed optimization. A commenter points out the restriction to self-recursive calls, arguing that true tail-call optimization should handle mutual recursion as well. The impact on stack introspection and debugging is also raised again, highlighting the challenges in preserving these features while implementing tail calls.

  Some commenters discuss alternative approaches to achieving similar performance gains without relying on tail-call optimization. One suggestion involves using generators or iterators, which can provide memory-efficient looping constructs. Another commenter mentions trampolining as a potential workaround, allowing for tail-call-like behavior without altering the stack.

  The performance implications of the change are also debated. While some acknowledge the potential benefits in specific scenarios, others question the overall impact on typical Python code. The benchmark presented in the original blog post is scrutinized, with some commenters suggesting it represents a contrived case and might not reflect real-world performance.

  Finally, some commenters offer insights into the broader context of tail-call optimization and its relevance in different programming paradigms. The cultural shift required for Python developers to adopt tail-recursive style is discussed, with some arguing that it goes against established Python practices. The distinction between proper tail calls and merely saving a stack frame is also mentioned, highlighting the nuances of implementing tail-call optimization correctly.